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公明
@@ -1299,10 +1299,36 @@
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"similarityThreshold": "Similarity threshold",
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"similarityPlaceholder": "0.7",
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"similarityHint": "Results below this value are filtered (0-1)",
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"hybridWeight": "Hybrid weight",
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"hybridPlaceholder": "0.7",
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"hybridHint": "Vector weight (0-1); 1.0 = vector only, 0.0 = keyword only",
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"subIndexFilter": "Sub-index filter (optional)",
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"subIndexFilterPlaceholder": "e.g. prod, must match an indexing sub_indexes tag",
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"subIndexFilterHint": "Empty = no filter. When set, only rows whose sub_indexes contain this tag (legacy rows with empty sub_indexes still match).",
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"postRetrieveHeader": "Post-retrieval (dedupe / budget)",
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"postRetrieveDedupeAuto": "Results are always deduped by normalized text (whitespace-collapsed bodies). No setting required.",
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"prefetchTopK": "Prefetch candidates (vector stage)",
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"prefetchTopKPlaceholder": "0",
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"prefetchTopKHint": "0 = same as Top-K; larger values fetch more vector hits before dedupe/truncate (max 200).",
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"maxContextChars": "Max returned characters (Unicode)",
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"maxContextCharsPlaceholder": "0",
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"maxContextCharsHint": "0 = unlimited; keeps whole chunks in rank order until the budget is exceeded.",
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"maxContextTokens": "Max returned tokens",
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"maxContextTokensPlaceholder": "0",
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"maxContextTokensHint": "0 = unlimited; tiktoken estimate (embedding model name, fallback cl100k_base).",
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"indexConfig": "Index config",
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"chunkStrategy": "Chunking strategy",
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"chunkStrategyMarkdownRecursive": "Markdown headers, then recursive (recommended)",
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"chunkStrategyRecursive": "Recursive only",
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"chunkStrategyHint": "Matches Eino-style pipelines: Markdown headers + recursive for docs; plain text can use recursive only.",
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"requestTimeoutSeconds": "Embedding HTTP timeout (seconds)",
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"requestTimeoutPlaceholder": "120",
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"requestTimeoutHint": "0 uses the default 120s embedding HTTP client timeout.",
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"batchSize": "Embedding batch size",
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"batchSizePlaceholder": "64",
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"batchSizeHint": "Max texts per embedding request (SQLite indexer batches writes accordingly).",
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"preferSourceFile": "Prefer on-disk source file when indexing (Eino FileLoader)",
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"preferSourceFileHint": "When enabled, content comes from file_path; falls back to DB content if load fails.",
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"subIndexes": "Eino sub-indexes (comma-separated)",
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"subIndexesPlaceholder": "e.g. prod, knowledge",
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"subIndexesHint": "Passed to indexer.WithSubIndexes; stored in the sub_indexes column.",
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"chunkSize": "Chunk size",
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"chunkSizePlaceholder": "512",
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"chunkSizeHint": "Max tokens per chunk (default 512)",
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@@ -1299,10 +1299,36 @@
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"similarityThreshold": "相似度阈值",
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"similarityPlaceholder": "0.7",
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"similarityHint": "相似度阈值（0-1），低于此值的结果将被过滤",
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"hybridWeight": "混合检索权重",
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"hybridPlaceholder": "0.7",
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"hybridHint": "向量检索的权重（0-1），1.0表示纯向量检索，0.0表示纯关键词检索",
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"subIndexFilter": "子索引过滤（可选）",
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"subIndexFilterPlaceholder": "如 prod，与索引 sub_indexes 一致",
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"subIndexFilterHint": "留空不过滤；填写后仅检索向量行 sub_indexes 中含该标签的结果（未打标旧行仍保留）。",
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"postRetrieveHeader": "检索后处理（去重 / 预算）",
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"postRetrieveDedupeAuto": "检索结果会自动按正文规范化去重（合并仅空白不同的重复片段），无需配置。",
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"prefetchTopK": "预取候选数（向量阶段）",
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"prefetchTopKPlaceholder": "0",
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"prefetchTopKHint": "0 表示与 Top-K 相同；大于 Top-K 时先多取候选再经去重/截断回到 Top-K（上限 200）。",
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"maxContextChars": "返回内容最大字符数（Unicode）",
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"maxContextCharsPlaceholder": "0",
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"maxContextCharsHint": "0 表示不限制；按检索顺序整段保留 chunk，超出则丢弃后续。",
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"maxContextTokens": "返回内容最大 Token 数",
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"maxContextTokensPlaceholder": "0",
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"maxContextTokensHint": "0 表示不限制；tiktoken 估算（与嵌入模型名一致，失败则用 cl100k_base）。",
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"indexConfig": "索引配置",
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"chunkStrategy": "分块策略",
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"chunkStrategyMarkdownRecursive": "Markdown 标题切分后递归（推荐）",
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"chunkStrategyRecursive": "仅递归切分",
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"chunkStrategyHint": "与 Eino 索引链一致：技术文档建议 Markdown 标题 + 递归；纯文本可仅用递归。",
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"requestTimeoutSeconds": "嵌入 HTTP 超时（秒）",
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"requestTimeoutPlaceholder": "120",
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"requestTimeoutHint": "0 表示使用默认 120 秒，与嵌入 HTTP 客户端一致。",
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"batchSize": "嵌入批大小",
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"batchSizePlaceholder": "64",
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"batchSizeHint": "单次嵌入请求的文本条数上限（SQLite 写入按此分批）。",
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"preferSourceFile": "索引时优先从磁盘源文件读取（Eino FileLoader）",
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"preferSourceFileHint": "开启后以 file_path 为准；读取失败时回退数据库中的 content。",
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"subIndexes": "Eino 子索引（逗号分隔）",
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"subIndexesPlaceholder": "例如: prod, knowledge",
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"subIndexesHint": "对应 indexer.WithSubIndexes，持久化到向量表 sub_indexes 字段。",
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"chunkSize": "分块大小（Chunk Size）",
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"chunkSizePlaceholder": "512",
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"chunkSizeHint": "每个块的最大 token 数（默认 512），长文本会被分割成多个块",
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+65
-18
@@ -182,15 +182,49 @@ async function loadConfig(loadTools = true) {
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retrievalThresholdInput.value = knowledge.retrieval?.similarity_threshold || 0.7;
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}
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const retrievalWeightInput = document.getElementById('knowledge-retrieval-hybrid-weight');
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if (retrievalWeightInput) {
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const hybridWeight = knowledge.retrieval?.hybrid_weight;
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// 允许0.0值，只有undefined/null时才使用默认值
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retrievalWeightInput.value = (hybridWeight !== undefined && hybridWeight !== null) ? hybridWeight : 0.7;
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const subIdxFilterInput = document.getElementById('knowledge-retrieval-sub-index-filter');
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if (subIdxFilterInput) {
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subIdxFilterInput.value = knowledge.retrieval?.sub_index_filter || '';
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}
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const post = knowledge.retrieval?.post_retrieve || {};
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const prefetchInput = document.getElementById('knowledge-post-retrieve-prefetch-top-k');
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if (prefetchInput) {
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prefetchInput.value = post.prefetch_top_k ?? 0;
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}
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const maxCharsInput = document.getElementById('knowledge-post-retrieve-max-chars');
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if (maxCharsInput) {
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maxCharsInput.value = post.max_context_chars ?? 0;
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}
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const maxTokInput = document.getElementById('knowledge-post-retrieve-max-tokens');
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if (maxTokInput) {
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maxTokInput.value = post.max_context_tokens ?? 0;
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}
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// 索引配置
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const indexing = knowledge.indexing || {};
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const chunkStrategySelect = document.getElementById('knowledge-indexing-chunk-strategy');
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if (chunkStrategySelect) {
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const v = (indexing.chunk_strategy || 'markdown_then_recursive').toLowerCase();
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chunkStrategySelect.value = v === 'recursive' ? 'recursive' : 'markdown_then_recursive';
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}
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const reqTimeoutInput = document.getElementById('knowledge-indexing-request-timeout');
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if (reqTimeoutInput) {
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reqTimeoutInput.value = indexing.request_timeout_seconds ?? 120;
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}
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const batchSizeInput = document.getElementById('knowledge-indexing-batch-size');
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if (batchSizeInput) {
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batchSizeInput.value = indexing.batch_size ?? 64;
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}
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const preferFileCb = document.getElementById('knowledge-indexing-prefer-source-file');
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if (preferFileCb) {
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preferFileCb.checked = indexing.prefer_source_file === true;
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}
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const subIdxInput = document.getElementById('knowledge-indexing-sub-indexes');
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if (subIdxInput) {
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const arr = indexing.sub_indexes;
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subIdxInput.value = Array.isArray(arr) ? arr.join(', ') : (typeof arr === 'string' ? arr : '');
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}
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const chunkSizeInput = document.getElementById('knowledge-indexing-chunk-size');
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if (chunkSizeInput) {
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chunkSizeInput.value = indexing.chunk_size || 512;
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@@ -811,20 +845,33 @@ async function applySettings() {
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const val = parseFloat(document.getElementById('knowledge-retrieval-similarity-threshold')?.value);
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return isNaN(val) ? 0.7 : val;
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})(),
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hybrid_weight: (() => {
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const val = parseFloat(document.getElementById('knowledge-retrieval-hybrid-weight')?.value);
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return isNaN(val) ? 0.7 : val; // 允许0.0值，只有NaN时才使用默认值
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})()
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sub_index_filter: document.getElementById('knowledge-retrieval-sub-index-filter')?.value?.trim() || '',
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post_retrieve: {
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prefetch_top_k: parseInt(document.getElementById('knowledge-post-retrieve-prefetch-top-k')?.value, 10) || 0,
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max_context_chars: parseInt(document.getElementById('knowledge-post-retrieve-max-chars')?.value, 10) || 0,
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max_context_tokens: parseInt(document.getElementById('knowledge-post-retrieve-max-tokens')?.value, 10) || 0
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}
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},
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indexing: {
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chunk_size: parseInt(document.getElementById("knowledge-indexing-chunk-size")?.value) || 512,
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chunk_overlap: parseInt(document.getElementById("knowledge-indexing-chunk-overlap")?.value) ?? 50,
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max_chunks_per_item: parseInt(document.getElementById("knowledge-indexing-max-chunks-per-item")?.value) ?? 0,
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max_rpm: parseInt(document.getElementById("knowledge-indexing-max-rpm")?.value) ?? 0,
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rate_limit_delay_ms: parseInt(document.getElementById("knowledge-indexing-rate-limit-delay-ms")?.value) ?? 300,
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max_retries: parseInt(document.getElementById("knowledge-indexing-max-retries")?.value) ?? 3,
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retry_delay_ms: parseInt(document.getElementById("knowledge-indexing-retry-delay-ms")?.value) ?? 1000
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}
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indexing: (() => {
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const subRaw = document.getElementById("knowledge-indexing-sub-indexes")?.value?.trim() || "";
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const sub_indexes = subRaw
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? subRaw.split(/[,，]/).map(s => s.trim()).filter(Boolean)
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: [];
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return {
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chunk_strategy: document.getElementById("knowledge-indexing-chunk-strategy")?.value || "markdown_then_recursive",
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request_timeout_seconds: parseInt(document.getElementById("knowledge-indexing-request-timeout")?.value, 10) || 0,
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batch_size: parseInt(document.getElementById("knowledge-indexing-batch-size")?.value, 10) || 0,
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prefer_source_file: document.getElementById("knowledge-indexing-prefer-source-file")?.checked === true,
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sub_indexes,
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chunk_size: parseInt(document.getElementById("knowledge-indexing-chunk-size")?.value) || 512,
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chunk_overlap: parseInt(document.getElementById("knowledge-indexing-chunk-overlap")?.value) ?? 50,
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max_chunks_per_item: parseInt(document.getElementById("knowledge-indexing-max-chunks-per-item")?.value) ?? 0,
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max_rpm: parseInt(document.getElementById("knowledge-indexing-max-rpm")?.value) ?? 0,
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rate_limit_delay_ms: parseInt(document.getElementById("knowledge-indexing-rate-limit-delay-ms")?.value) ?? 300,
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max_retries: parseInt(document.getElementById("knowledge-indexing-max-retries")?.value) ?? 3,
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retry_delay_ms: parseInt(document.getElementById("knowledge-indexing-retry-delay-ms")?.value) ?? 1000
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};
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})()
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};
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const wecomAgentIdVal = document.getElementById('robot-wecom-agent-id')?.value.trim();
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